We compare DLGNet multi-turn dialogue performance with existing state-of-the-art dialogue models including (V)HREDimplementation obtained from https://github.com/julianser/hed-dlg-truncated [1]}, [2]}, DAIMimplementation obtained from https://github.com/dreasysnail/converse_GAN [3]}, hredGAN [4]}, and aBoots [5]}. Note that DAIM is single turn and does not use a multi-turn dialogue context, but we have included it here for completeness. We compare how the models perform based on informativeness (a combination of relevance and diversity metrics) of generated responses. For relevance, we adopted BLEU-2 [6]} and ROUGE-2 [7]} scores. For diversity, we adopted distinct unigram (DIST-1) and bigram (DIST-2) [8]} scores as well as normalized average sequence length (NASL), similar to [4]} Olabiyi2018.
